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Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients
The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intel...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9408009/ https://www.ncbi.nlm.nih.gov/pubmed/36011155 http://dx.doi.org/10.3390/healthcare10081498 |
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author | Tsai, Wei-Chun Liu, Chung-Feng Lin, Hung-Jung Hsu, Chien-Chin Ma, Yu-Shan Chen, Chia-Jung Huang, Chien-Cheng Chen, Chia-Chun |
author_facet | Tsai, Wei-Chun Liu, Chung-Feng Lin, Hung-Jung Hsu, Chien-Chin Ma, Yu-Shan Chen, Chia-Jung Huang, Chien-Cheng Chen, Chia-Chun |
author_sort | Tsai, Wei-Chun |
collection | PubMed |
description | The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC’s AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard’s ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments. |
format | Online Article Text |
id | pubmed-9408009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94080092022-08-26 Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients Tsai, Wei-Chun Liu, Chung-Feng Lin, Hung-Jung Hsu, Chien-Chin Ma, Yu-Shan Chen, Chia-Jung Huang, Chien-Cheng Chen, Chia-Chun Healthcare (Basel) Article The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC’s AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard’s ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments. MDPI 2022-08-09 /pmc/articles/PMC9408009/ /pubmed/36011155 http://dx.doi.org/10.3390/healthcare10081498 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tsai, Wei-Chun Liu, Chung-Feng Lin, Hung-Jung Hsu, Chien-Chin Ma, Yu-Shan Chen, Chia-Jung Huang, Chien-Cheng Chen, Chia-Chun Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients |
title | Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients |
title_full | Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients |
title_fullStr | Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients |
title_full_unstemmed | Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients |
title_short | Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients |
title_sort | design and implementation of a comprehensive ai dashboard for real-time prediction of adverse prognosis of ed patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9408009/ https://www.ncbi.nlm.nih.gov/pubmed/36011155 http://dx.doi.org/10.3390/healthcare10081498 |
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